Histogramas - R Base
## [1] -0.01330762
## location value
## 1 col -1.8719792
## 2 col -0.3383812
## 3 col 0.3548935
## 4 col 1.6680366
## 5 col -0.2856015
## 6 col -1.3252798
## 'data.frame': 1000 obs. of 2 variables:
## $ location: chr "col" "col" "col" "col" ...
## $ value : num -1.872 -0.338 0.355 1.668 -0.286 ...


Histogramas - ggplot
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.0.6 ✓ dplyr 1.0.4
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## location value
## 1 col -1.8719792
## 2 col -0.3383812
## 3 col 0.3548935
## 4 col 1.6680366
## 5 col -0.2856015
## 6 col -1.3252798
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## location value
## 1 col -1.8719792
## 2 col -0.3383812
## 3 col 0.3548935
## 4 col 1.6680366
## 5 col -0.2856015
## 6 col -1.3252798

## location value
## 1 col -1.0822942
## 2 col -1.4719386
## 3 col -2.2040994
## 4 col 0.3562852
## 5 col -1.7147851
## 6 col -0.4913932
## location value
## 1995 peru 3.133988
## 1996 peru 3.650295
## 1997 peru 4.134690
## 1998 peru 2.449526
## 1999 peru 4.631226
## 2000 peru 4.739639
## [1] 2000 2

Densidades
## location value
## 1 col -1.8719792
## 2 col -0.3383812
## 3 col 0.3548935
## 4 col 1.6680366
## 5 col -0.2856015
## 6 col -1.3252798


Graficas de dispersión
## colombia peru argentina us brasil
## 1 -11.186951 -35.814125 39.20946 104.60548 102.61603
## 2 9.020724 -34.978722 -40.59333 162.99971 142.77937
## 3 5.864960 21.674366 34.05761 39.80808 71.36377
## 4 -6.035194 8.437587 -74.27318 75.57745 70.20479
## 5 -1.157605 19.558368 -11.67167 125.23552 172.42622
## 6 -1.504274 21.438675 -16.80262 56.71725 100.80200
## [1] 100
## [1] 100 5
## colombia peru argentina us brasil
## 1 -11.186951 -35.814125 39.20946 104.60548 102.61603
## 2 9.020724 -34.978722 -40.59333 162.99971 142.77937
## 3 5.864960 21.674366 34.05761 39.80808 71.36377
## 4 -6.035194 8.437587 -74.27318 75.57745 70.20479
## 5 -1.157605 19.558368 -11.67167 125.23552 172.42622
## 6 -1.504274 21.438675 -16.80262 56.71725 100.80200
## colombia peru argentina us brasil year
## 1 -11.186951 -35.814125 39.20946 104.60548 102.61603 2018
## 2 9.020724 -34.978722 -40.59333 162.99971 142.77937 2019
## 3 5.864960 21.674366 34.05761 39.80808 71.36377 2020
## 4 -6.035194 8.437587 -74.27318 75.57745 70.20479 2021
## 5 -1.157605 19.558368 -11.67167 125.23552 172.42622 2018
## 6 -1.504274 21.438675 -16.80262 56.71725 100.80200 2019
## 'data.frame': 100 obs. of 6 variables:
## $ colombia : num -11.19 9.02 5.86 -6.04 -1.16 ...
## $ peru : num -35.81 -34.98 21.67 8.44 19.56 ...
## $ argentina: num 39.2 -40.6 34.1 -74.3 -11.7 ...
## $ us : num 104.6 163 39.8 75.6 125.2 ...
## $ brasil : num 102.6 142.8 71.4 70.2 172.4 ...
## $ year : int 2018 2019 2020 2021 2018 2019 2020 2021 2018 2019 ...


## 'data.frame': 100 obs. of 6 variables:
## $ colombia : num -11.19 9.02 5.86 -6.04 -1.16 ...
## $ peru : num -35.81 -34.98 21.67 8.44 19.56 ...
## $ argentina: num 39.2 -40.6 34.1 -74.3 -11.7 ...
## $ us : num 104.6 163 39.8 75.6 125.2 ...
## $ brasil : num 102.6 142.8 71.4 70.2 172.4 ...
## $ year : Factor w/ 4 levels "2018","2019",..: 1 2 3 4 1 2 3 4 1 2 ...

## `geom_smooth()` using formula 'y ~ x'

## `geom_smooth()` using formula 'y ~ x'

Ahora combinemoslas!!!!!!!!
## 'data.frame': 100 obs. of 6 variables:
## $ colombia : num -11.19 9.02 5.86 -6.04 -1.16 ...
## $ peru : num -35.81 -34.98 21.67 8.44 19.56 ...
## $ argentina: num 39.2 -40.6 34.1 -74.3 -11.7 ...
## $ us : num 104.6 163 39.8 75.6 125.2 ...
## $ brasil : num 102.6 142.8 71.4 70.2 172.4 ...
## $ year : Factor w/ 4 levels "2018","2019",..: 1 2 3 4 1 2 3 4 1 2 ...
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'


Correlaciones
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning in ggcorr(data3, method = c("everything", "pearson")): data in column(s)
## 'year' are not numeric and were ignored

## colombia peru argentina us brasil year
## 1 -11.186951 -35.814125 39.20946 104.60548 102.61603 2018
## 2 9.020724 -34.978722 -40.59333 162.99971 142.77937 2019
## 3 5.864960 21.674366 34.05761 39.80808 71.36377 2020
## 4 -6.035194 8.437587 -74.27318 75.57745 70.20479 2021
## 5 -1.157605 19.558368 -11.67167 125.23552 172.42622 2018
## 6 -1.504274 21.438675 -16.80262 56.71725 100.80200 2019
## 'data.frame': 100 obs. of 6 variables:
## $ colombia : num -11.19 9.02 5.86 -6.04 -1.16 ...
## $ peru : num -35.81 -34.98 21.67 8.44 19.56 ...
## $ argentina: num 39.2 -40.6 34.1 -74.3 -11.7 ...
## $ us : num 104.6 163 39.8 75.6 125.2 ...
## $ brasil : num 102.6 142.8 71.4 70.2 172.4 ...
## $ year : Factor w/ 4 levels "2018","2019",..: 1 2 3 4 1 2 3 4 1 2 ...



## corrplot 0.84 loaded
## 'data.frame': 100 obs. of 6 variables:
## $ colombia : num -11.19 9.02 5.86 -6.04 -1.16 ...
## $ peru : num -35.81 -34.98 21.67 8.44 19.56 ...
## $ argentina: num 39.2 -40.6 34.1 -74.3 -11.7 ...
## $ us : num 104.6 163 39.8 75.6 125.2 ...
## $ brasil : num 102.6 142.8 71.4 70.2 172.4 ...
## $ year : Factor w/ 4 levels "2018","2019",..: 1 2 3 4 1 2 3 4 1 2 ...
## colombia peru argentina us brasil
## colombia 1.0000000 0.8015215 0.4913152 -0.5146297 -0.6879268
## peru 0.8015215 1.0000000 0.4161253 -0.5147450 -0.6510829
## argentina 0.4913152 0.4161253 1.0000000 -0.1693722 -0.3159770
## us -0.5146297 -0.5147450 -0.1693722 1.0000000 0.4635345
## brasil -0.6879268 -0.6510829 -0.3159770 0.4635345 1.0000000








## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.000000e+00 1.297798e-23 2.099041e-07 4.315180e-08 2.641572e-15
## [2,] 1.297798e-23 0.000000e+00 1.663033e-05 4.280265e-08 2.238362e-13
## [3,] 2.099041e-07 1.663033e-05 0.000000e+00 9.206154e-02 1.362356e-03
## [4,] 4.315180e-08 4.280265e-08 9.206154e-02 0.000000e+00 1.193377e-06
## [5,] 2.641572e-15 2.238362e-13 1.362356e-03 1.193377e-06 0.000000e+00


Mucho mas!!!!
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